Update README to version 0.4

Former-commit-id: 592d5e76

Update README to version 0.4
Former-commit-id: 592d5e76
a7726a25 · Jean-Luc Parouty · 21be2ae0 · a7726a25 · a7726a25
Commit a7726a25 authored 5 years ago by Jean-Luc Parouty
--- a/README.ipynb
+++ b/README.ipynb
@@ -29,7 +29,7 @@
       " - Understanding **Tensorflow/Keras** and **Jupyter lab** technologies\n",
       " - Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU\n",
       "\n",
-       "Current Version : 0.2\n",
+       "Current Version : 0.4\n",
       "\n",
       "## Course materials\n",
       "**[<img width=\"50px\" src=\"fidle/img/00-Fidle-pdf.svg\"></img>\n",

 %% Cell type:code id: tags:

 ``` python
 from IPython.display import display,Markdown
 display(Markdown(open('README.md', 'r').read()))
 #
 # This README is visible under Jupiter LAb ! :-)
 ```

 %% Output

    [<img width="600px" src="fidle/img/00-Fidle-titre-01.svg"></img>](#)
    
    <!-- --------------------------------------------------- -->
    <!-- To correctly view this README under Jupyter Lab     -->
    <!-- Open the notebook: README.ipynb!                    -->
    <!-- --------------------------------------------------- -->
    
    ## A propos
    
    This repository contains all the documents and links of the **Fidle Training**.
    
    The objectives of this training, co-organized by the Formation Permanente CNRS and the SARI and DEVLOG networks, are :
     - Understanding the **bases of Deep Learning** neural networks
     - Develop a **first experience** through simple and representative examples
     - Understanding **Tensorflow/Keras** and **Jupyter lab** technologies
     - Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU
    
-    Current Version : 0.2
+    Current Version : 0.4
    
    ## Course materials
    **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img>
    Get the course slides](https://cloud.univ-grenoble-alpes.fr/index.php/s/f5T59gk3bxm4Dt9)**
    
    
    
    <!-- ![pdf](fidle/img/00-Fidle-pdf.png) -->
    Useful information is also available in the [wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/home)
    
    
    ## Jupyter notebooks
    
    [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/git/https%3A%2F%2Fgricad-gitlab.univ-grenoble-alpes.fr%2Ftalks%2Fdeeplearning.git/master?urlpath=lab/tree/index.ipynb)
    
    
    <!-- DO NOT REMOVE THIS TAG !!! -->
    <!-- INDEX -->
    <!-- INDEX_BEGIN -->
    [[LINR1] - Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Direct determination of linear regression
    [[GRAD1] - Linear regression with gradient descent](LinearReg/02-Gradient-descent.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;An example of gradient descent in the simple case of a linear regression.
    [[POLR1] - Complexity Syndrome](LinearReg/03-Polynomial-Regression.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Illustration of the problem of complexity with the polynomial regression
    [[LOGR1] - Logistic regression, in pure Tensorflow](LinearReg/04-Logistic-Regression.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Logistic Regression with Mini-Batch Gradient Descent using pure TensorFlow.
    [[PER57] - Perceptron Model 1957](IRIS/01-Simple-Perceptron.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A simple perceptron, with the IRIS dataset.
    [[BHP1] - Regression with a Dense Network (DNN)](BHPD/01-DNN-Regression.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A Simple regression with a Dense Neural Network (DNN) - BHPD dataset
    [[BHP2] - Regression with a Dense Network (DNN) - Advanced code](BHPD/02-DNN-Regression-Premium.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;More advanced example of DNN network code - BHPD dataset
    [[MNIST1] - Simple classification with DNN](MNIST/01-DNN-MNIST.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Example of classification with a fully connected neural network
    [[GTS1] - CNN with GTSRB dataset - Data analysis and preparation](GTSRB/01-Preparation-of-data.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 1 : Data analysis and creation of a usable dataset
    [[GTS2] - CNN with GTSRB dataset - First convolutions](GTSRB/02-First-convolutions.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 2 : First convolutions and first results
    [[GTS3] - CNN with GTSRB dataset - Monitoring ](GTSRB/03-Tracking-and-visualizing.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 3 : Monitoring and analysing training, managing checkpoints
    [[GTS4] - CNN with GTSRB dataset - Data augmentation ](GTSRB/04-Data-augmentation.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 4 : Improving the results with data augmentation
    [[GTS5] - CNN with GTSRB dataset - Full convolutions ](GTSRB/05-Full-convolutions.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 5 : A lot of models, a lot of datasets and a lot of results.
    [[GTS6] - CNN with GTSRB dataset - Full convolutions as a batch](GTSRB/06-Full-convolutions-batch.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 6 : Run Full convolution notebook as a batch
    [[GTS7] - Full convolutions Report](GTSRB/07-Full-convolutions-reports.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 7 : Displaying the reports of the different jobs
    [[TSB1] - Tensorboard with/from Jupyter ](GTSRB/99-Scripts-Tensorboard.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;4 ways to use Tensorboard from the Jupyter environment
    [[IMDB1] - Text embedding with IMDB](IMDB/01-Embedding-Keras.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;A very classical example of word embedding for text classification (sentiment analysis)
    [[IMDB2] - Text embedding with IMDB - Reloaded](IMDB/02-Prediction.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Example of reusing a previously saved model
    [[IMDB3] - Text embedding/LSTM model with IMDB](IMDB/03-LSTM-Keras.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Still the same problem, but with a network combining embedding and LSTM
    [[SYNOP1] - Time series with RNN - Preparation of data](SYNOP/01-Preparation-of-data.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 1 : Data analysis and creation of a usable dataset
    [[SYNOP2] - Time series with RNN - Try a prediction](SYNOP/02-First-predictions.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 2 : Training session and first predictions
    [[SYNOP3] - Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 3: Attempt to predict in the longer term
    [[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 1 : Model construction and Training
    [[VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 2 : Exploring our latent space
    [[VAE3] - About the CelebA dataset](VAE/03-About-CelebA.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 3 : About the CelebA dataset, a more fun dataset !
    [[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-batch.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 4 : Preparation of a clustered dataset, batchable
    [[VAE5] - Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 5 :\tChecking the clustered dataset
    [[VAE6] - Variational AutoEncoder (VAE) with CelebA (small)](VAE/06-VAE-with-CelebA-s.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 6 : Variational AutoEncoder (VAE) with CelebA (small res.)
    [[VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 7 : Variational AutoEncoder (VAE) with CelebA (medium res.)
    [[VAE8] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/08-VAE-withCelebA-post.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Episode 8 : Exploring latent space of our trained models
    [[BASH1] - OAR batch script](VAE/batch-oar.sh)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Bash script for OAR batch submission of a notebook
    [[BASH2] - SLURM batch script](VAE/batch-slurm.sh)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Bash script for SLURM batch submission of a notebook
    [[ACTF1] - Activation functions](Misc/Activation-Functions.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Some activation functions, with their derivatives.
    [[NP1] - A short introduction to Numpy](Misc/Numpy.ipynb)
    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Numpy is an essential tool for the Scientific Python.
    <!-- INDEX_END -->
    
    
    ## Installation
    
    A procedure for **configuring** and **starting Jupyter** is available in the **[Wiki](https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/-/wikis/howto-jupyter)**.
    
    ## Licence
    
    [<img width="100px" src="fidle/img/00-fidle-CC BY-NC-SA.svg"></img>](https://creativecommons.org/licenses/by-nc-sa/4.0/)
    \[en\] Attribution - NonCommercial - ShareAlike 4.0 International (CC BY-NC-SA 4.0)
    \[Fr\] Attribution - Pas d’Utilisation Commerciale - Partage dans les Mêmes Conditions 4.0 International
    See [License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
    See [Disclaimer](https://creativecommons.org/licenses/by-nc-sa/4.0/#).
    
    
    ----
    [<img width="80px" src="fidle/img/00-Fidle-logo-01.svg"></img>](#)

 %% Cell type:code id: tags:

 ``` python
 ```

--- a/README.md
+++ b/README.md
@@ -15,7 +15,7 @@ The objectives of this training, co-organized by the Formation Permanente CNRS a
 - Understanding **Tensorflow/Keras** and **Jupyter lab** technologies
 - Apprehend the **academic computing environments** Tier-2 or Tier-1 with powerfull GPU

-Current Version : 0.2
+Current Version : 0.4

 ## Course materials
 **[<img width="50px" src="fidle/img/00-Fidle-pdf.svg"></img>